Overview

Dataset statistics

Number of variables8
Number of observations710
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.5 KiB
Average record size in memory64.2 B

Variable types

DateTime1
Numeric7

Alerts

HML is highly correlated with Mkt_RF and 3 other fieldsHigh correlation
CMA is highly correlated with Mkt_RF and 2 other fieldsHigh correlation
Mkt_RF is highly correlated with SMB and 2 other fieldsHigh correlation
SMB is highly correlated with Mkt_RF and 3 other fieldsHigh correlation
RMW is highly correlated with SMB and 3 other fieldsHigh correlation
MOM is highly correlated with SMB and 1 other fieldsHigh correlation
Date has unique values Unique
RF has 69 (9.7%) zeros Zeros

Reproduction

Analysis started2022-10-11 15:44:23.402270
Analysis finished2022-10-11 15:44:27.682267
Duration4.28 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct710
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Minimum1963-07-01 00:00:00
Maximum2022-08-01 00:00:00
2022-10-11T11:44:27.722134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.773991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Mkt_RF
Real number (ℝ)

HIGH CORRELATION

Distinct566
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005572535211
Minimum-0.2324
Maximum0.161
Zeros1
Zeros (%)0.1%
Negative285
Negative (%)40.1%
Memory size5.7 KiB
2022-10-11T11:44:27.832522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.2324
5-th percentile-0.072375
Q1-0.019675
median0.00915
Q30.034
95-th percentile0.070895
Maximum0.161
Range0.3934
Interquartile range (IQR)0.053675

Descriptive statistics

Standard deviation0.04477172841
Coefficient of variation (CV)8.034355408
Kurtosis1.83251363
Mean0.005572535211
Median Absolute Deviation (MAD)0.02695
Skewness-0.5032989172
Sum3.9565
Variance0.002004507665
MonotonicityNot monotonic
2022-10-11T11:44:27.881545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.01383
 
0.4%
-0.01443
 
0.4%
0.01033
 
0.4%
0.0143
 
0.4%
0.00783
 
0.4%
-0.02293
 
0.4%
0.06933
 
0.4%
0.01433
 
0.4%
0.03113
 
0.4%
0.02063
 
0.4%
Other values (556)680
95.8%
ValueCountFrequency (%)
-0.23241
0.1%
-0.17231
0.1%
-0.16081
0.1%
-0.13391
0.1%
-0.1291
0.1%
-0.12751
0.1%
-0.11911
0.1%
-0.11771
0.1%
-0.111
0.1%
-0.10721
0.1%
ValueCountFrequency (%)
0.1611
0.1%
0.13661
0.1%
0.13651
0.1%
0.12472
0.3%
0.12161
0.1%
0.11351
0.1%
0.1131
0.1%
0.11141
0.1%
0.10841
0.1%
0.10281
0.1%

SMB
Real number (ℝ)

HIGH CORRELATION

Distinct510
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00227056338
Minimum-0.1535
Maximum0.1834
Zeros2
Zeros (%)0.3%
Negative340
Negative (%)47.9%
Memory size5.7 KiB
2022-10-11T11:44:27.933163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.1535
5-th percentile-0.042955
Q1-0.015175
median0.001
Q30.02035
95-th percentile0.04914
Maximum0.1834
Range0.3369
Interquartile range (IQR)0.035525

Descriptive statistics

Standard deviation0.03024648984
Coefficient of variation (CV)13.32113876
Kurtosis3.135512646
Mean0.00227056338
Median Absolute Deviation (MAD)0.018
Skewness0.3422554032
Sum1.6121
Variance0.0009148501478
MonotonicityNot monotonic
2022-10-11T11:44:28.061248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00135
 
0.7%
0.02714
 
0.6%
-0.01394
 
0.6%
-0.01144
 
0.6%
0.00314
 
0.6%
-0.00624
 
0.6%
-0.00413
 
0.4%
0.01923
 
0.4%
-0.01073
 
0.4%
-0.00053
 
0.4%
Other values (500)673
94.8%
ValueCountFrequency (%)
-0.15351
0.1%
-0.10021
0.1%
-0.08311
0.1%
-0.08071
0.1%
-0.07281
0.1%
-0.06931
0.1%
-0.06911
0.1%
-0.06821
0.1%
-0.06451
0.1%
-0.06431
0.1%
ValueCountFrequency (%)
0.18341
0.1%
0.12911
0.1%
0.10411
0.1%
0.09931
0.1%
0.09181
0.1%
0.0911
0.1%
0.08511
0.1%
0.07991
0.1%
0.07611
0.1%
0.07541
0.1%

HML
Real number (ℝ)

HIGH CORRELATION

Distinct498
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00298084507
Minimum-0.1397
Maximum0.1275
Zeros0
Zeros (%)0.0%
Negative326
Negative (%)45.9%
Memory size5.7 KiB
2022-10-11T11:44:28.111435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.1397
5-th percentile-0.041
Q1-0.013875
median0.00245
Q30.0175
95-th percentile0.05401
Maximum0.1275
Range0.2672
Interquartile range (IQR)0.031375

Descriptive statistics

Standard deviation0.02966002661
Coefficient of variation (CV)9.950207377
Kurtosis2.379070988
Mean0.00298084507
Median Absolute Deviation (MAD)0.0158
Skewness0.1068899396
Sum2.1164
Variance0.0008797171784
MonotonicityNot monotonic
2022-10-11T11:44:28.162137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00857
 
1.0%
0.01174
 
0.6%
0.00154
 
0.6%
0.01754
 
0.6%
-0.00024
 
0.6%
-0.00134
 
0.6%
0.00434
 
0.6%
0.01194
 
0.6%
0.02274
 
0.6%
-0.02763
 
0.4%
Other values (488)668
94.1%
ValueCountFrequency (%)
-0.13971
0.1%
-0.11291
0.1%
-0.09871
0.1%
-0.0971
0.1%
-0.08431
0.1%
-0.08331
0.1%
-0.08321
0.1%
-0.07821
0.1%
-0.07661
0.1%
-0.06951
0.1%
ValueCountFrequency (%)
0.12751
0.1%
0.12481
0.1%
0.12321
0.1%
0.08631
0.1%
0.08411
0.1%
0.0831
0.1%
0.08281
0.1%
0.08191
0.1%
0.08171
0.1%
0.07631
0.1%

RMW
Real number (ℝ)

HIGH CORRELATION

Distinct446
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002724225352
Minimum-0.1873
Maximum0.1309
Zeros1
Zeros (%)0.1%
Negative309
Negative (%)43.5%
Memory size5.7 KiB
2022-10-11T11:44:28.211672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.1873
5-th percentile-0.027485
Q1-0.007875
median0.0024
Q30.013075
95-th percentile0.03471
Maximum0.1309
Range0.3182
Interquartile range (IQR)0.02095

Descriptive statistics

Standard deviation0.02215376522
Coefficient of variation (CV)8.132133858
Kurtosis11.54272923
Mean0.002724225352
Median Absolute Deviation (MAD)0.0106
Skewness-0.2997310721
Sum1.9342
Variance0.0004907893136
MonotonicityNot monotonic
2022-10-11T11:44:28.259025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0037
 
1.0%
0.0135
 
0.7%
0.00275
 
0.7%
0.01315
 
0.7%
-0.00685
 
0.7%
0.02044
 
0.6%
0.00084
 
0.6%
-0.01374
 
0.6%
0.00934
 
0.6%
-0.00424
 
0.6%
Other values (436)663
93.4%
ValueCountFrequency (%)
-0.18731
0.1%
-0.09211
0.1%
-0.08321
0.1%
-0.0761
0.1%
-0.07061
0.1%
-0.06311
0.1%
-0.0481
0.1%
-0.0472
0.3%
-0.04621
0.1%
-0.04441
0.1%
ValueCountFrequency (%)
0.13091
0.1%
0.11821
0.1%
0.0961
0.1%
0.09111
0.1%
0.08061
0.1%
0.07661
0.1%
0.07421
0.1%
0.07221
0.1%
0.06461
0.1%
0.06291
0.1%

CMA
Real number (ℝ)

HIGH CORRELATION

Distinct443
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002843380282
Minimum-0.0694
Maximum0.0905
Zeros1
Zeros (%)0.1%
Negative329
Negative (%)46.3%
Memory size5.7 KiB
2022-10-11T11:44:28.308093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0694
5-th percentile-0.026555
Q1-0.01
median0.00095
Q30.0149
95-th percentile0.03681
Maximum0.0905
Range0.1599
Interquartile range (IQR)0.0249

Descriptive statistics

Standard deviation0.02039952998
Coefficient of variation (CV)7.174393842
Kurtosis1.426598638
Mean0.002843380282
Median Absolute Deviation (MAD)0.01255
Skewness0.3021728958
Sum2.0188
Variance0.0004161408235
MonotonicityNot monotonic
2022-10-11T11:44:28.355863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.00346
 
0.8%
0.00095
 
0.7%
0.0095
 
0.7%
-0.00045
 
0.7%
0.00845
 
0.7%
-0.0124
 
0.6%
0.00464
 
0.6%
-0.0164
 
0.6%
-0.00334
 
0.6%
-0.00954
 
0.6%
Other values (433)664
93.5%
ValueCountFrequency (%)
-0.06941
0.1%
-0.06771
0.1%
-0.06621
0.1%
-0.05831
0.1%
-0.05661
0.1%
-0.05631
0.1%
-0.051
0.1%
-0.04741
0.1%
-0.0471
0.1%
-0.04541
0.1%
ValueCountFrequency (%)
0.09051
0.1%
0.08391
0.1%
0.07711
0.1%
0.06561
0.1%
0.06461
0.1%
0.06211
0.1%
0.05921
0.1%
0.05911
0.1%
0.05891
0.1%
0.05651
0.1%

MOM
Real number (ℝ)

HIGH CORRELATION

Distinct539
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006294225352
Minimum-0.343
Maximum0.182
Zeros1
Zeros (%)0.1%
Negative264
Negative (%)37.2%
Memory size5.7 KiB
2022-10-11T11:44:28.405817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.343
5-th percentile-0.06532
Q1-0.009525
median0.00735
Q30.028975
95-th percentile0.064205
Maximum0.182
Range0.525
Interquartile range (IQR)0.0385

Descriptive statistics

Standard deviation0.0419055474
Coefficient of variation (CV)6.657776781
Kurtosis9.952008228
Mean0.006294225352
Median Absolute Deviation (MAD)0.0192
Skewness-1.283579652
Sum4.4689
Variance0.001756074903
MonotonicityNot monotonic
2022-10-11T11:44:28.456352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03165
 
0.7%
0.00225
 
0.7%
0.00044
 
0.6%
0.00864
 
0.6%
-0.00584
 
0.6%
0.0093
 
0.4%
0.02523
 
0.4%
0.04453
 
0.4%
0.03033
 
0.4%
-0.01843
 
0.4%
Other values (529)673
94.8%
ValueCountFrequency (%)
-0.3431
0.1%
-0.2531
0.1%
-0.16331
0.1%
-0.13821
0.1%
-0.12491
0.1%
-0.12431
0.1%
-0.11871
0.1%
-0.11571
0.1%
-0.1071
0.1%
-0.09551
0.1%
ValueCountFrequency (%)
0.1821
0.1%
0.1661
0.1%
0.15221
0.1%
0.13221
0.1%
0.12751
0.1%
0.12571
0.1%
0.11481
0.1%
0.10381
0.1%
0.09981
0.1%
0.09641
0.1%

RF
Real number (ℝ≥0)

ZEROS

Distinct106
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003626338028
Minimum0
Maximum0.0135
Zeros69
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2022-10-11T11:44:28.505425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0014
median0.0038
Q30.0051
95-th percentile0.0081
Maximum0.0135
Range0.0135
Interquartile range (IQR)0.0037

Descriptive statistics

Standard deviation0.002682255718
Coefficient of variation (CV)0.7396595953
Kurtosis0.6327734726
Mean0.003626338028
Median Absolute Deviation (MAD)0.00175
Skewness0.6596971813
Sum2.5747
Variance7.194495739 × 10-6
MonotonicityNot monotonic
2022-10-11T11:44:28.557740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
069
 
9.7%
0.000144
 
6.2%
0.004321
 
3.0%
0.00421
 
3.0%
0.004218
 
2.5%
0.004618
 
2.5%
0.003916
 
2.3%
0.003116
 
2.3%
0.004416
 
2.3%
0.003715
 
2.1%
Other values (96)456
64.2%
ValueCountFrequency (%)
069
9.7%
0.000144
6.2%
0.00028
 
1.1%
0.00034
 
0.6%
0.00042
 
0.3%
0.00051
 
0.1%
0.00065
 
0.7%
0.00076
 
0.8%
0.00087
 
1.0%
0.00097
 
1.0%
ValueCountFrequency (%)
0.01351
 
0.1%
0.01311
 
0.1%
0.01281
 
0.1%
0.01261
 
0.1%
0.01242
0.3%
0.01213
0.4%
0.01151
 
0.1%
0.01131
 
0.1%
0.01081
 
0.1%
0.01072
0.3%

Interactions

2022-10-11T11:44:27.250306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.098031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.610851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.932955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.241238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.555769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.942920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.298408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.181383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.658835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.979743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.289197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.668927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.989072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.346123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.249438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.705191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.024386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.333839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.716375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.033116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.390162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.317806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.748926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.065945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.376354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.760477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.075355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.435415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.468057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.794326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.108970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.420908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.805928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.118326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.482495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.517294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.841319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.154049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.467117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.851563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.163456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.526364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.563069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:25.886140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.196479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.510715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:26.896208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-11T11:44:27.205769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-11T11:44:28.600643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-11T11:44:28.649814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-11T11:44:28.699707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-11T11:44:28.750174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-11T11:44:27.596504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-11T11:44:27.659510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateMkt_RFSMBHMLRMWCMAMOMRF
01963-07-01-0.0039-0.0041-0.00970.0068-0.01180.00900.0027
11963-08-010.0507-0.00800.01800.0036-0.00350.01010.0025
21963-09-01-0.0157-0.00520.0013-0.00710.00290.00190.0027
31963-10-010.0253-0.0139-0.00100.0280-0.02010.03120.0029
41963-11-01-0.0085-0.00880.0175-0.00510.0224-0.00740.0027
51963-12-010.0183-0.0210-0.00020.0003-0.00070.01750.0029
61964-01-010.02240.00130.01480.00170.01470.00860.0030
71964-02-010.01540.00280.0281-0.00050.00910.00260.0026
81964-03-010.01410.01230.0340-0.02210.03220.00750.0031
91964-04-010.0010-0.0152-0.0067-0.0127-0.0108-0.00580.0029

Last rows

DateMkt_RFSMBHMLRMWCMAMOMRF
7002021-11-01-0.0155-0.0176-0.00440.07220.01740.00900.0000
7012021-12-010.0310-0.00770.03280.04920.0443-0.02600.0001
7022022-01-01-0.0625-0.04050.12750.00870.0771-0.02590.0000
7032022-02-01-0.02290.02960.0304-0.02080.03130.01760.0000
7042022-03-010.0305-0.0215-0.0180-0.01560.03170.03000.0001
7052022-04-01-0.0946-0.00400.06190.03630.05920.04890.0001
7062022-05-01-0.0034-0.00060.08410.01440.03980.02480.0003
7072022-06-01-0.08430.0130-0.05970.0185-0.04700.00790.0006
7082022-07-010.09570.0187-0.04100.0068-0.0694-0.03960.0008
7092022-08-01-0.03780.01510.0031-0.04800.01310.02090.0019